| Chapter title |
Integrative Analysis of Proteomics Data to Obtain Clinically Relevant Markers
|
|---|---|
| Chapter number | 94 |
| Book title |
Tissue Proteomics
|
| Published in |
Methods in molecular biology, November 2017
|
| DOI | 10.1007/7651_2017_94 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7852-6, 978-1-4939-7854-0
|
| Authors |
Nathan Salomonis, Salomonis, Nathan |
| Abstract |
The analysis of proteomics data can be significantly challenging. Beyond the technical challenges of accurately identifying and quantifying peptides, identifying the most biologically coherent set of biomarkers can be a particularly daunting step. In this chapter, we will review a series of methods implemented in the software AltAnalyze that can be used to normalize proteomics peptide counts, identify a minimal set of the most distinguishing morbidity-associated biomarkers, and connect up these results to known pathways and interacting protein and regulatory networks. Here, we will apply this workflow to two examples that highlight different benefits of an integrated analysis workflow: (1) urine proteomics samples from patients with distinct kidney transplantation morbidities and (2) sudden infant death syndrome. By the end of this chapter, the reader should be able to apply a similar workflow to their own datasets to identify biologically significant protein markers and relevant networks. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 60% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 22 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 23% |
| Student > Master | 4 | 18% |
| Other | 2 | 9% |
| Researcher | 2 | 9% |
| Lecturer | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 7 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 32% |
| Biochemistry, Genetics and Molecular Biology | 2 | 9% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 9% |
| Computer Science | 2 | 9% |
| Agricultural and Biological Sciences | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 8 | 36% |